Big Data and Healthcare

The application of a Big Data strategy is critical to the Healthcare industry more than other industries. Big Data is both a problem and a solution in healthcare. It's problem because the sheer volume of data is currently unstructured, fragmented and unusable. Putting aside the question of capacity to host this data, the effort involved in structuring the data into usable formats presents a challenge.

Nevertheless the benefits of Big Data to Healthcare are two fold. On the one hand, Business Intelligence software can help identify weaknesses is a payer's commercial strategy and help advance products with the a dynamic understanding of market data. Secondly, a Big Data strategy can help with providing evidence based healthcare that reduces costs and delivers better results to our members.

As Jordan Robertson argues in "The Healthcare Industry Turns to Big Data":

As hospitals digitize patient records and amass huge amounts of data, many are turning to companies such as Microsoft, SAS, Dell (DELL), IBM (IBM), and Oracle (ORCL) for their data-mining expertise, which can help medical providers perform detective work and improve care. The so-called Big Data business has already permeated other industries and generated more than $30 billion in revenues last year, according to research firm IDC. It’s expected to grow to close to $34 billion this year in part because of increased use in the health-care industry. Crunching numbers is potentially good business for hospitals as well. By making “meaningful use” of computer systems, they’re eligible for millions of dollars in government funding from the Obama administration’s $14.6 billion program launched in 2009 to encourage adoption of electronic medical records.

This WSJ article summarizes the challenges and opportunities well:


Kaveh Safavi, who leads Accenture's ACN -0.56% health practice for North America, says big data gives two benefits to clinicians. First is "the ability to see information across time in ways that aren't possible.

"The second is to begin the process of pattern recognition, particularly when you are looking for low frequency events, or things that where the signal is very small and might not be discernible when looking at very small groups," he says. A well known example of this is the ability for Google to track the progress of flu through looking at search terms.